# Copyright 2025 The HunyuanVideo Team and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import re from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import torch from transformers import ByT5Tokenizer, Qwen2_5_VLTextModel, Qwen2Tokenizer, T5EncoderModel from ...guiders import ClassifierFreeGuidance from ...models import AutoencoderKLHunyuanVideo15, HunyuanVideo15Transformer3DModel from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import is_torch_xla_available, logging, replace_example_docstring from ...utils.torch_utils import randn_tensor from ..pipeline_utils import DiffusionPipeline from .image_processor import HunyuanVideo15ImageProcessor from .pipeline_output import HunyuanVideo15PipelineOutput if is_torch_xla_available(): import torch_xla.core.xla_model as xm XLA_AVAILABLE = True else: XLA_AVAILABLE = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```python >>> import torch >>> from diffusers import HunyuanVideo15Pipeline >>> from diffusers.utils import export_to_video >>> model_id = "hunyuanvideo-community/HunyuanVideo-1.5-480p_t2v" >>> pipe = HunyuanVideo15Pipeline.from_pretrained(model_id, torch_dtype=torch.float16) >>> pipe.vae.enable_tiling() >>> pipe.to("cuda") >>> output = pipe( ... prompt="A cat walks on the grass, realistic", ... num_inference_steps=50, ... ).frames[0] >>> export_to_video(output, "output.mp4", fps=15) ``` """ def format_text_input(prompt: List[str], system_message: str) -> List[Dict[str, Any]]: """ Apply text to template. Args: prompt (List[str]): Input text. system_message (str): System message. Returns: List[Dict[str, Any]]: List of chat conversation. """ template = [ [{"role": "system", "content": system_message}, {"role": "user", "content": p if p else " "}] for p in prompt ] return template def extract_glyph_texts(prompt: str) -> List[str]: """ Extract glyph texts from prompt using regex pattern. Args: prompt: Input prompt string Returns: List of extracted glyph texts """ pattern = r"\"(.*?)\"|“(.*?)”" matches = re.findall(pattern, prompt) result = [match[0] or match[1] for match in matches] result = list(dict.fromkeys(result)) if len(result) > 1 else result if result: formatted_result = ". ".join([f'Text "{text}"' for text in result]) + ". " else: formatted_result = None return formatted_result # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, sigmas: Optional[List[float]] = None, **kwargs, ): r""" Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`List[int]`, *optional*): Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`. sigmas (`List[float]`, *optional*): Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` must be `None`. Returns: `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") if timesteps is not None: accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" timestep schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" sigmas schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps = scheduler.timesteps return timesteps, num_inference_steps class HunyuanVideo15Pipeline(DiffusionPipeline): r""" Pipeline for text-to-video generation using HunyuanVideo1.5. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). Args: transformer ([`HunyuanVideo15Transformer3DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded video latents. scheduler ([`FlowMatchEulerDiscreteScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded video latents. vae ([`AutoencoderKLHunyuanVideo15`]): Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. text_encoder ([`Qwen2.5-VL-7B-Instruct`]): [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct), specifically the [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) variant. tokenizer (`Qwen2Tokenizer`): Tokenizer of class [Qwen2Tokenizer]. text_encoder_2 ([`T5EncoderModel`]): [T5EncoderModel](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel) variant. tokenizer_2 (`ByT5Tokenizer`): Tokenizer of class [ByT5Tokenizer] guider ([`ClassifierFreeGuidance`]): [ClassifierFreeGuidance]for classifier free guidance. """ model_cpu_offload_seq = "text_encoder->transformer->vae" def __init__( self, text_encoder: Qwen2_5_VLTextModel, tokenizer: Qwen2Tokenizer, transformer: HunyuanVideo15Transformer3DModel, vae: AutoencoderKLHunyuanVideo15, scheduler: FlowMatchEulerDiscreteScheduler, text_encoder_2: T5EncoderModel, tokenizer_2: ByT5Tokenizer, guider: ClassifierFreeGuidance, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, transformer=transformer, scheduler=scheduler, text_encoder_2=text_encoder_2, tokenizer_2=tokenizer_2, guider=guider, ) self.vae_scale_factor_temporal = self.vae.temporal_compression_ratio if getattr(self, "vae", None) else 4 self.vae_scale_factor_spatial = self.vae.spatial_compression_ratio if getattr(self, "vae", None) else 16 self.video_processor = HunyuanVideo15ImageProcessor(vae_scale_factor=self.vae_scale_factor_spatial) self.target_size = self.transformer.config.target_size if getattr(self, "transformer", None) else 640 self.vision_states_dim = ( self.transformer.config.image_embed_dim if getattr(self, "transformer", None) else 1152 ) self.num_channels_latents = self.vae.config.latent_channels if hasattr(self, "vae") else 32 # fmt: off self.system_message = "You are a helpful assistant. Describe the video by detailing the following aspects: \ 1. The main content and theme of the video. \ 2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \ 3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \ 4. background environment, light, style and atmosphere. \ 5. camera angles, movements, and transitions used in the video." # fmt: on self.prompt_template_encode_start_idx = 108 self.tokenizer_max_length = 1000 self.tokenizer_2_max_length = 256 self.vision_num_semantic_tokens = 729 self.default_aspect_ratio = (16, 9) # (width: height) @staticmethod def _get_mllm_prompt_embeds( text_encoder: Qwen2_5_VLTextModel, tokenizer: Qwen2Tokenizer, prompt: Union[str, List[str]], device: torch.device, tokenizer_max_length: int = 1000, num_hidden_layers_to_skip: int = 2, # fmt: off system_message: str = "You are a helpful assistant. Describe the video by detailing the following aspects: \ 1. The main content and theme of the video. \ 2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \ 3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \ 4. background environment, light, style and atmosphere. \ 5. camera angles, movements, and transitions used in the video.", # fmt: on crop_start: int = 108, ) -> Tuple[torch.Tensor, torch.Tensor]: prompt = [prompt] if isinstance(prompt, str) else prompt prompt = format_text_input(prompt, system_message) text_inputs = tokenizer.apply_chat_template( prompt, add_generation_prompt=True, tokenize=True, return_dict=True, padding="max_length", max_length=tokenizer_max_length + crop_start, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids.to(device=device) prompt_attention_mask = text_inputs.attention_mask.to(device=device) prompt_embeds = text_encoder( input_ids=text_input_ids, attention_mask=prompt_attention_mask, output_hidden_states=True, ).hidden_states[-(num_hidden_layers_to_skip + 1)] if crop_start is not None and crop_start > 0: prompt_embeds = prompt_embeds[:, crop_start:] prompt_attention_mask = prompt_attention_mask[:, crop_start:] return prompt_embeds, prompt_attention_mask @staticmethod def _get_byt5_prompt_embeds( tokenizer: ByT5Tokenizer, text_encoder: T5EncoderModel, prompt: Union[str, List[str]], device: torch.device, tokenizer_max_length: int = 256, ): prompt = [prompt] if isinstance(prompt, str) else prompt glyph_texts = [extract_glyph_texts(p) for p in prompt] prompt_embeds_list = [] prompt_embeds_mask_list = [] for glyph_text in glyph_texts: if glyph_text is None: glyph_text_embeds = torch.zeros( (1, tokenizer_max_length, text_encoder.config.d_model), device=device, dtype=text_encoder.dtype ) glyph_text_embeds_mask = torch.zeros((1, tokenizer_max_length), device=device, dtype=torch.int64) else: txt_tokens = tokenizer( glyph_text, padding="max_length", max_length=tokenizer_max_length, truncation=True, add_special_tokens=True, return_tensors="pt", ).to(device) glyph_text_embeds = text_encoder( input_ids=txt_tokens.input_ids, attention_mask=txt_tokens.attention_mask.float(), )[0] glyph_text_embeds = glyph_text_embeds.to(device=device) glyph_text_embeds_mask = txt_tokens.attention_mask.to(device=device) prompt_embeds_list.append(glyph_text_embeds) prompt_embeds_mask_list.append(glyph_text_embeds_mask) prompt_embeds = torch.cat(prompt_embeds_list, dim=0) prompt_embeds_mask = torch.cat(prompt_embeds_mask_list, dim=0) return prompt_embeds, prompt_embeds_mask def encode_prompt( self, prompt: Union[str, List[str]], device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, batch_size: int = 1, num_videos_per_prompt: int = 1, prompt_embeds: Optional[torch.Tensor] = None, prompt_embeds_mask: Optional[torch.Tensor] = None, prompt_embeds_2: Optional[torch.Tensor] = None, prompt_embeds_mask_2: Optional[torch.Tensor] = None, ): r""" Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device batch_size (`int`): batch size of prompts, defaults to 1 num_images_per_prompt (`int`): number of images that should be generated per prompt prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. If not provided, text embeddings will be generated from `prompt` input argument. prompt_embeds_mask (`torch.Tensor`, *optional*): Pre-generated text mask. If not provided, text mask will be generated from `prompt` input argument. prompt_embeds_2 (`torch.Tensor`, *optional*): Pre-generated glyph text embeddings from ByT5. If not provided, will be generated from `prompt` input argument using self.tokenizer_2 and self.text_encoder_2. prompt_embeds_mask_2 (`torch.Tensor`, *optional*): Pre-generated glyph text mask from ByT5. If not provided, will be generated from `prompt` input argument using self.tokenizer_2 and self.text_encoder_2. """ device = device or self._execution_device dtype = dtype or self.text_encoder.dtype if prompt is None: prompt = [""] * batch_size prompt = [prompt] if isinstance(prompt, str) else prompt if prompt_embeds is None: prompt_embeds, prompt_embeds_mask = self._get_mllm_prompt_embeds( tokenizer=self.tokenizer, text_encoder=self.text_encoder, prompt=prompt, device=device, tokenizer_max_length=self.tokenizer_max_length, system_message=self.system_message, crop_start=self.prompt_template_encode_start_idx, ) if prompt_embeds_2 is None: prompt_embeds_2, prompt_embeds_mask_2 = self._get_byt5_prompt_embeds( tokenizer=self.tokenizer_2, text_encoder=self.text_encoder_2, prompt=prompt, device=device, tokenizer_max_length=self.tokenizer_2_max_length, ) _, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) prompt_embeds_mask = prompt_embeds_mask.repeat(1, num_videos_per_prompt, 1) prompt_embeds_mask = prompt_embeds_mask.view(batch_size * num_videos_per_prompt, seq_len) _, seq_len_2, _ = prompt_embeds_2.shape prompt_embeds_2 = prompt_embeds_2.repeat(1, num_videos_per_prompt, 1) prompt_embeds_2 = prompt_embeds_2.view(batch_size * num_videos_per_prompt, seq_len_2, -1) prompt_embeds_mask_2 = prompt_embeds_mask_2.repeat(1, num_videos_per_prompt, 1) prompt_embeds_mask_2 = prompt_embeds_mask_2.view(batch_size * num_videos_per_prompt, seq_len_2) prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) prompt_embeds_mask = prompt_embeds_mask.to(dtype=dtype, device=device) prompt_embeds_2 = prompt_embeds_2.to(dtype=dtype, device=device) prompt_embeds_mask_2 = prompt_embeds_mask_2.to(dtype=dtype, device=device) return prompt_embeds, prompt_embeds_mask, prompt_embeds_2, prompt_embeds_mask_2 def check_inputs( self, prompt, height, width, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, prompt_embeds_mask=None, negative_prompt_embeds_mask=None, prompt_embeds_2=None, prompt_embeds_mask_2=None, negative_prompt_embeds_2=None, negative_prompt_embeds_mask_2=None, ): if height is None and width is not None: raise ValueError("If `width` is provided, `height` also have to be provided.") elif width is None and height is not None: raise ValueError("If `height` is provided, `width` also have to be provided.") if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and prompt_embeds_mask is None: raise ValueError( "If `prompt_embeds` are provided, `prompt_embeds_mask` also have to be passed. Make sure to generate `prompt_embeds_mask` from the same text encoder that was used to generate `prompt_embeds`." ) if negative_prompt_embeds is not None and negative_prompt_embeds_mask is None: raise ValueError( "If `negative_prompt_embeds` are provided, `negative_prompt_embeds_mask` also have to be passed. Make sure to generate `negative_prompt_embeds_mask` from the same text encoder that was used to generate `negative_prompt_embeds`." ) if prompt is None and prompt_embeds_2 is None: raise ValueError( "Provide either `prompt` or `prompt_embeds_2`. Cannot leave both `prompt` and `prompt_embeds_2` undefined." ) if prompt_embeds_2 is not None and prompt_embeds_mask_2 is None: raise ValueError( "If `prompt_embeds_2` are provided, `prompt_embeds_mask_2` also have to be passed. Make sure to generate `prompt_embeds_mask_2` from the same text encoder that was used to generate `prompt_embeds_2`." ) if negative_prompt_embeds_2 is not None and negative_prompt_embeds_mask_2 is None: raise ValueError( "If `negative_prompt_embeds_2` are provided, `negative_prompt_embeds_mask_2` also have to be passed. Make sure to generate `negative_prompt_embeds_mask_2` from the same text encoder that was used to generate `negative_prompt_embeds_2`." ) def prepare_latents( self, batch_size: int, num_channels_latents: int = 32, height: int = 720, width: int = 1280, num_frames: int = 129, dtype: Optional[torch.dtype] = None, device: Optional[torch.device] = None, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, ) -> torch.Tensor: if latents is not None: return latents.to(device=device, dtype=dtype) shape = ( batch_size, num_channels_latents, (num_frames - 1) // self.vae_scale_factor_temporal + 1, int(height) // self.vae_scale_factor_spatial, int(width) // self.vae_scale_factor_spatial, ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) return latents def prepare_cond_latents_and_mask(self, latents, dtype: Optional[torch.dtype], device: Optional[torch.device]): """ Prepare conditional latents and mask for t2v generation. Args: latents: Main latents tensor (B, C, F, H, W) Returns: tuple: (cond_latents_concat, mask_concat) - both are zero tensors for t2v """ batch, channels, frames, height, width = latents.shape cond_latents_concat = torch.zeros(batch, channels, frames, height, width, dtype=dtype, device=device) mask_concat = torch.zeros(batch, 1, frames, height, width, dtype=dtype, device=device) return cond_latents_concat, mask_concat @property def num_timesteps(self): return self._num_timesteps @property def attention_kwargs(self): return self._attention_kwargs @property def current_timestep(self): return self._current_timestep @property def interrupt(self): return self._interrupt @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, negative_prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_frames: int = 121, num_inference_steps: int = 50, sigmas: List[float] = None, num_videos_per_prompt: Optional[int] = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, prompt_embeds_mask: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds_mask: Optional[torch.Tensor] = None, prompt_embeds_2: Optional[torch.Tensor] = None, prompt_embeds_mask_2: Optional[torch.Tensor] = None, negative_prompt_embeds_2: Optional[torch.Tensor] = None, negative_prompt_embeds_mask_2: Optional[torch.Tensor] = None, output_type: Optional[str] = "np", return_dict: bool = True, attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds` instead. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. height (`int`, *optional*): The height in pixels of the generated video. width (`int`, *optional*): The width in pixels of the generated video. num_frames (`int`, defaults to `121`): The number of frames in the generated video. num_inference_steps (`int`, defaults to `50`): The number of denoising steps. More denoising steps usually lead to a higher quality video at the expense of slower inference. sigmas (`List[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. num_videos_per_prompt (`int`, *optional*, defaults to 1): The number of videos to generate per prompt. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.Tensor`, *optional*): Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied random `generator`. prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument. prompt_embeds_mask (`torch.Tensor`, *optional*): Pre-generated mask for prompt embeddings. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. negative_prompt_embeds_mask (`torch.Tensor`, *optional*): Pre-generated mask for negative prompt embeddings. prompt_embeds_2 (`torch.Tensor`, *optional*): Pre-generated text embeddings from the second text encoder. Can be used to easily tweak text inputs. prompt_embeds_mask_2 (`torch.Tensor`, *optional*): Pre-generated mask for prompt embeddings from the second text encoder. negative_prompt_embeds_2 (`torch.Tensor`, *optional*): Pre-generated negative text embeddings from the second text encoder. negative_prompt_embeds_mask_2 (`torch.Tensor`, *optional*): Pre-generated mask for negative prompt embeddings from the second text encoder. output_type (`str`, *optional*, defaults to `"np"`): The output format of the generated video. Choose between "np", "pt", or "latent". return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`HunyuanVideo15PipelineOutput`] instead of a plain tuple. attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). Examples: Returns: [`~HunyuanVideo15PipelineOutput`] or `tuple`: If `return_dict` is `True`, [`HunyuanVideo15PipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated videos. """ # 1. Check inputs. Raise error if not correct self.check_inputs( prompt=prompt, height=height, width=width, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, prompt_embeds_mask=prompt_embeds_mask, negative_prompt_embeds_mask=negative_prompt_embeds_mask, prompt_embeds_2=prompt_embeds_2, prompt_embeds_mask_2=prompt_embeds_mask_2, negative_prompt_embeds_2=negative_prompt_embeds_2, negative_prompt_embeds_mask_2=negative_prompt_embeds_mask_2, ) if height is None and width is None: height, width = self.video_processor.calculate_default_height_width( self.default_aspect_ratio[1], self.default_aspect_ratio[0], self.target_size ) self._attention_kwargs = attention_kwargs self._current_timestep = None self._interrupt = False device = self._execution_device # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # 3. Encode input prompt prompt_embeds, prompt_embeds_mask, prompt_embeds_2, prompt_embeds_mask_2 = self.encode_prompt( prompt=prompt, device=device, dtype=self.transformer.dtype, batch_size=batch_size, num_videos_per_prompt=num_videos_per_prompt, prompt_embeds=prompt_embeds, prompt_embeds_mask=prompt_embeds_mask, prompt_embeds_2=prompt_embeds_2, prompt_embeds_mask_2=prompt_embeds_mask_2, ) if self.guider._enabled and self.guider.num_conditions > 1: ( negative_prompt_embeds, negative_prompt_embeds_mask, negative_prompt_embeds_2, negative_prompt_embeds_mask_2, ) = self.encode_prompt( prompt=negative_prompt, device=device, dtype=self.transformer.dtype, batch_size=batch_size, num_videos_per_prompt=num_videos_per_prompt, prompt_embeds=negative_prompt_embeds, prompt_embeds_mask=negative_prompt_embeds_mask, prompt_embeds_2=negative_prompt_embeds_2, prompt_embeds_mask_2=negative_prompt_embeds_mask_2, ) # 4. Prepare timesteps sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas) # 5. Prepare latent variables latents = self.prepare_latents( batch_size * num_videos_per_prompt, self.num_channels_latents, height, width, num_frames, self.transformer.dtype, device, generator, latents, ) cond_latents_concat, mask_concat = self.prepare_cond_latents_and_mask(latents, self.transformer.dtype, device) image_embeds = torch.zeros( batch_size, self.vision_num_semantic_tokens, self.vision_states_dim, dtype=self.transformer.dtype, device=device, ) # 7. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue self._current_timestep = t latent_model_input = torch.cat([latents, cond_latents_concat, mask_concat], dim=1) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]).to(latent_model_input.dtype) # Step 1: Collect model inputs needed for the guidance method # conditional inputs should always be first element in the tuple guider_inputs = { "encoder_hidden_states": (prompt_embeds, negative_prompt_embeds), "encoder_attention_mask": (prompt_embeds_mask, negative_prompt_embeds_mask), "encoder_hidden_states_2": (prompt_embeds_2, negative_prompt_embeds_2), "encoder_attention_mask_2": (prompt_embeds_mask_2, negative_prompt_embeds_mask_2), } # Step 2: Update guider's internal state for this denoising step self.guider.set_state(step=i, num_inference_steps=num_inference_steps, timestep=t) # Step 3: Prepare batched model inputs based on the guidance method # The guider splits model inputs into separate batches for conditional/unconditional predictions. # For CFG with guider_inputs = {"encoder_hidden_states": (prompt_embeds, negative_prompt_embeds)}: # you will get a guider_state with two batches: # guider_state = [ # {"encoder_hidden_states": prompt_embeds, "__guidance_identifier__": "pred_cond"}, # conditional batch # {"encoder_hidden_states": negative_prompt_embeds, "__guidance_identifier__": "pred_uncond"}, # unconditional batch # ] # Other guidance methods may return 1 batch (no guidance) or 3+ batches (e.g., PAG, APG). guider_state = self.guider.prepare_inputs(guider_inputs) # Step 4: Run the denoiser for each batch # Each batch in guider_state represents a different conditioning (conditional, unconditional, etc.). # We run the model once per batch and store the noise prediction in guider_state_batch.noise_pred. for guider_state_batch in guider_state: self.guider.prepare_models(self.transformer) # Extract conditioning kwargs for this batch (e.g., encoder_hidden_states) cond_kwargs = { input_name: getattr(guider_state_batch, input_name) for input_name in guider_inputs.keys() } # e.g. "pred_cond"/"pred_uncond" context_name = getattr(guider_state_batch, self.guider._identifier_key) with self.transformer.cache_context(context_name): # Run denoiser and store noise prediction in this batch guider_state_batch.noise_pred = self.transformer( hidden_states=latent_model_input, image_embeds=image_embeds, timestep=timestep, attention_kwargs=self.attention_kwargs, return_dict=False, **cond_kwargs, )[0] # Cleanup model (e.g., remove hooks) self.guider.cleanup_models(self.transformer) # Step 5: Combine predictions using the guidance method # The guider takes all noise predictions from guider_state and combines them according to the guidance algorithm. # Continuing the CFG example, the guider receives: # guider_state = [ # {"encoder_hidden_states": prompt_embeds, "noise_pred": noise_pred_cond, "__guidance_identifier__": "pred_cond"}, # batch 0 # {"encoder_hidden_states": negative_prompt_embeds, "noise_pred": noise_pred_uncond, "__guidance_identifier__": "pred_uncond"}, # batch 1 # ] # And extracts predictions using the __guidance_identifier__: # pred_cond = guider_state[0]["noise_pred"] # extracts noise_pred_cond # pred_uncond = guider_state[1]["noise_pred"] # extracts noise_pred_uncond # Then applies CFG formula: # noise_pred = pred_uncond + guidance_scale * (pred_cond - pred_uncond) # Returns GuiderOutput(pred=noise_pred, pred_cond=pred_cond, pred_uncond=pred_uncond) noise_pred = self.guider(guider_state)[0] # compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] if latents.dtype != latents_dtype: if torch.backends.mps.is_available(): # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 latents = latents.to(latents_dtype) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if XLA_AVAILABLE: xm.mark_step() self._current_timestep = None # 8. decode the latents to video and postprocess if not output_type == "latent": latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor video = self.vae.decode(latents, return_dict=False)[0] video = self.video_processor.postprocess_video(video, output_type=output_type) else: video = latents # Offload all models self.maybe_free_model_hooks() if not return_dict: return (video,) return HunyuanVideo15PipelineOutput(frames=video)